# Policies package¶

Policies module : contains all the (single-player) bandits algorithms:

Note

The list above might not be complete, see the details below.

All policies have the same interface, as described in BasePolicy, in order to use them in any experiment with the following approach:

my_policy = Policy(nbArms)
my_policy.startGame()  # start the game
for t in range(T):
chosen_arm_t = k_t = my_policy.choice()  # chose one arm
reward_t     = sampled from an arm k_t   # sample a reward
my_policy.getReward(k_t, reward_t)       # give it the the policy

Policies.klucb_mapping = {'Bernoulli': <built-in function klucbBern>, 'Exponential': <built-in function klucbExp>, 'Gamma': <built-in function klucbGamma>, 'Gaussian': <built-in function klucbGauss>, 'Poisson': <built-in function klucbPoisson>}

Maps name of arms to kl functions